Relevant bibliographies by topics / User Personalization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'User Personalization'

Author: Grafiati
Published: 7 June 2025
Last updated: 17 July 2025

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Journal articles on the topic "User Personalization"

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Amit, Gupta. "Personalization Techniques for Tailoring User Experience in Mobile Apps." European Journal of Advances in Engineering and Technology 8, no. 8 (2021): 84–93. https://doi.org/10.5281/zenodo.11438582.

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Personalization in mobile applications has become a critical factor in enhancing user engagement and satisfaction. As users increasingly seek tailored experiences that cater to their individual preferences and needs, app developers are turning to advanced personalization techniques to meet these demands. This paper explores a variety of personalization strategies, their impact on user experience, and presents both quantitative and qualitative data to support these findings. The techniques examined include content personalization, UI/UX customization, notification tailoring, and location-based services. Through a comprehensive literature review, comparative analysis, and user feedback, the study aims to identify best practices and future trends in mobile app personalization. By delving into the methods used to collect and analyze user data, as well as the challenges and opportunities presented by these approaches, this research provides a thorough understanding of how personalization can be effectively implemented to maximize user satisfaction and engagement. The insights gained from this study will be valuable for app developers, marketers, and researchers interested in the evolving landscape of mobile application personalization.
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Petersen, Francoise, Giovanni Bartolomeo, and Mike Pluke. "Personalization and User Profile Management." International Journal of Interactive Mobile Technologies (iJIM) 2, no. 4 (2008): 25. http://dx.doi.org/10.3991/ijim.v2i4.666.

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Personalization and effective user profile management will be critical to meet the individual usersâ?? needs and for achieving e-Inclusion and e-Accessibility. This paper outlines means to achieve the goal of the new ICT era where services and devices can be personalized by the users in order to meet their needs and preferences, in various situations. Behind every instance of personalization is a profile that stores the user preferences, context of use and other information that can be used to deliver a user experience tailored to their individual needs and preferences. Next Generation Networks (NGN) and the convergence between telephony and Internet services offer a wide range of new terminal and service definition possibilities, and a much wider range of application in society. This paper describes the personalization and profile management activities at European Telecommunications Standards Institute (ETSI) Technical Committee Human Factors, together with relevant experimentations in recent European research projects.
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Rohit Sharma. "Data-Driven Personalization : Revolutionizing User Experience." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 868–77. http://dx.doi.org/10.32628/cseit241051075.

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Data-driven personalization has emerged as a transformative approach in digital user experience design, leveraging advanced analytics and machine learning to tailor content and interfaces to individual users. This article explores five key aspects of data-driven personalization: user behavior analysis, segmentation and targeting, machine learning algorithms, real-time adaptation, and privacy and ethical considerations. It examines the significant impact of personalization on business outcomes, including increased revenue and customer engagement, while also addressing implementation challenges, such as technological complexity and privacy concerns. The article provides insights into the methodologies, processes, and best practices for effective personalization, supported by industry statistics and case studies, offering a comprehensive overview of how organizations can harness this powerful approach to create more engaging, relevant, and effective digital experiences.
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Xiaoan, Zhan, Xu Yang, and Liu Yingchia. "Personalized UI Layout Generation using Deep Learning: An Adaptive Interface Design Approach for Enhanced User Experience." International Journal of Engineering and Management Research 14, no. 5 (2024): 134–47. https://doi.org/10.5281/zenodo.14190245.

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This study presents a new approach to personalized UI design using deep learning techniques to improve user experience through interface customization. We propose a hybrid VAE-GAN architecture combining variational autoencoders and generative adversarial networks to create coherent and user-specific UI layouts. The system includes user-friendly electronic models that capture personal preferences and behaviors, enabling real-time personalization of interactions. Our methodology leverages large-scale UI design datasets, and user interaction logs to train and evaluate the model. Experimental results demonstrate significant improvements in layout quality, personalization accuracy, and user satisfaction compared to existing approaches. A customer research study with 200 participants from different cultures proves the effectiveness of the personalization model in real situations. The system achieves a personalization accuracy of 0.89 ± 0.03 and a transfer speed of 1.2s ± 0.1s, the most efficient state-of-the-art UI personalization system. In addition, we discuss the theoretical implications of our approach to UI/UX design principles, potential business applications, and ethical considerations around AI-driven identity. This research contributes to advancing adaptive interface design and opens up new ways to integrate deep learning with UI/UX processes.
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Sai Kumar Bitra. "Ethical AI and privacy in digital personalization: balancing personalization and user trust." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 774–79. https://doi.org/10.30574/wjaets.2025.15.2.0596.

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This article explores the complex intersection of artificial intelligence, personalization, and privacy in digital environments, exploring how organizations can effectively balance personalized user experiences with ethical considerations and regulatory compliance. The article shows key challenges in this domain, including regulatory frameworks like GDPR and CCPA, ethical concerns such as algorithmic bias and discrimination, and the growing importance of zero-party data as a user-centric approach to data collection. The article further analyzes how explainable AI (XAI) frameworks can address the "black box" nature of AI systems while building user trust. Through article analysis of current literature and industry practices, this article provides strategic recommendations for implementing responsible AI personalization that respects user privacy, maintains transparency, and establishes trust-based relationships between organizations and their users in an increasingly AI-driven digital ecosystem.
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Ünlü, Sudenaz Ceren. "Enhancing User Experience through AI-Driven Personalization in User Interfaces." Human Computer Interaction 8, no. 1 (2024): 19. http://dx.doi.org/10.62802/m7mqmb52.

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Artificial intelligence (AI) has revolutionized user interface (UI) design by introducing personalization techniques that cater to individual user preferences, behaviors, and contexts. This research explores the integration of AI-driven personalization in user interfaces to enhance user experience (UX), focusing on adaptive design, predictive analytics, and real-time customization. By leveraging machine learning algorithms and behavioral data, AI enables interfaces to evolve dynamically, aligning with the unique needs of each user. This study investigates the role of personalization in improving engagement, satisfaction, and efficiency across various applications, such as e-commerce platforms, healthcare systems, and educational tools. Additionally, it examines the challenges of implementing personalized interfaces, including privacy concerns, data ethics, and algorithmic bias. By addressing these challenges, the research aims to develop best practices for ethical AI integration in user-centered design. The findings contribute to the growing body of knowledge on AI’s transformative potential in creating intuitive, efficient, and user-friendly interfaces, ultimately redefining the standards for digital interaction.
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Choeh, Joon Yeon, and Hong Joo Lee. "Mobile push personalization and user experience." AI Communications 21, no. 2-3 (2008): 185–93. http://dx.doi.org/10.3233/aic-2008-0435.

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Schiaffino, Silvia, and Analı́a Amandi. "User – interface agent interaction: personalization issues." International Journal of Human-Computer Studies 60, no. 1 (2004): 129–48. http://dx.doi.org/10.1016/j.ijhcs.2003.09.003.

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Lavie, Talia, Michal Sela, Ilit Oppenheim, Ohad Inbar, and Joachim Meyer. "User attitudes towards news content personalization." International Journal of Human-Computer Studies 68, no. 8 (2010): 483–95. http://dx.doi.org/10.1016/j.ijhcs.2009.09.011.

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De Bra, Paul. "Challenges in User Modeling and Personalization." IEEE Intelligent Systems 32, no. 5 (2017): 76–80. http://dx.doi.org/10.1109/mis.2017.3711638.

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Dissertations / Theses on the topic "User Personalization"

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Asif, Muhammad. "Personalization of Mobile Services." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25576.

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The mobile era is well established and the number of smartphone users is showing exponential growth. The capability of smartphones and enabling technologies is also increasing and has opened many possibilities of personalized mobile services. The goal of personalization is to support the user by providing the right service at the rightmoment. Early focus of personalization was on content adaptations in different information systems. The new approaches of personalization are still needed for mobileservices as it is a compelling feature of mobile communication systems for both endusers and service providers.Personalization is providing a means of fulfilling users’ needs more effectively andefficiently and, consequently increasing users’ satisfaction. By providing successfulpersonalization, a high degree of user satisfaction and a pleasant user experience can beachieved. Some features of personalization can cause problems and may outweigh thebenefits of personalization.This thesis has focused on how to achieve scrutable mobile client-side personalizationwhile keeping the user’s privacy. The issue of privacy in personalization of mobileservices can be reduced by shifting the control of their personal information towards theusers. Our research goal is to understand and improve the personalization process anddevelop an architecture for scrutable mobile client-side personalization while keepingthe user s’ privacy. Moreover, there is a need to develop an evaluation framework tomeasure the effectiveness of mobile services personalization. A design science researchmethodology is adopted in this research work. More particular contributions of thethesis are as follows: C1: Identifications of the research issues and challenges in personalization of mobileservices. C2: An approach for delivering personalized mobile services. C3: Development of mobile client-side personalization architecture. C4: Development of mobile services Personalization Evaluation Model. C5: Identification of the prospects of scrutable personalization of mobile services.
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Shankar, Anil K. "Simple user-context for better application personalization." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433351.

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Md, Amin Mohd Afandi. "A user acceptance model of web personalization systems." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/98965/1/Mohd%20Afandi%20bin%20Md%20Amin%20Thesis.PDF.

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Research on web personalization techniques for collecting and analysing web data in order to deliver personalized information to users is in an advanced state. Many metrics from the computational intelligence field have been developed to evaluate the algorithmic performance of Web Personalization Systems (WPSs). However, measuring the success of a WPS in terms of user acceptance is difficult until the WPS is deployed in practice. In summary, many techniques exist for delivering personalized information to a user, but a comprehensive measure of the success in WPSs in terms of human interaction and behaviour does not exist. This study aims to develop a framework for measuring user acceptance of WPSs from a user perspective. The proposed framework is based on the unified theory of acceptance and use of technology (UTAUT). The antecedents of user acceptance are described by indicators based on four key constructs, i.e. performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). All these constructs are underpinned by Information Systems (IS) theories that determine the intention to use (BI) and the actual use (USE) of a technology. A user acceptance model was proposed and validated using structural equation modelling (SEM) via the partial least squares path modelling (PLS-PM). Four user characteristics (i.e. gender, age, skill and experience) have been chosen for testing the moderating effects of the four constructs. The relationship between the four constructs in regard to BI and USE has been validated through moderating effects, in order to present an overall view of the extent of user acceptance of a WPS. Results from response data analysis show that the acceptance of a WPS is determined through PE, EE SI, and FC. The gender of a user was found to moderate the relationship between performance expectancy of a WPS and their behavioural intention in using a WPS. The effect of behavioural intention on the use of WPS is higher for a group of females than for males. Furthermore, the proposed model has been tested and validated for its explanation power of the model and effect size. The current study concluded that predictive relevance of intention to use a WPS is more effective than the actual WPS usage, which indicated that intention to use has more prediction power for describing a user acceptance of a WPS. The implications of these measures from the computational intelligent point of view are useful when a WPS is implemented. For example, the designer of a WPS should consider personalized design features that enable the delivery of relevant information, sharing to other users, and accessibility across many platforms, Such features create a better web experience and a complete security policy. These measures can be utilized to obtain a higher attention rate and continued use by a user; the features that define user acceptance of a WPS.
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Anderson, Corin R. "A machine learning approach to Web personalization /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6875.

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Abel, Fabian [Verfasser]. "Contextualization, user modeling and personalization in the social web : from social tagging via context to cross-system user modeling and personalization / Fabian Abel." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2011. http://d-nb.info/1014252423/34.

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Deng, Lin. "Mining user preference using SPY voting for search engine personalization /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20DENG.

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Vieira, André Fonseca dos Santos Dias. "Context-aware personalization environment for mobile computing." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8649.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>Currently, we live in a world where the amount of on-line information vastly outstrips any individual’s capability to survey it. Filtering that information in order to obtain only useful and interesting information is a solution to this problem. The mobile computing area proposes to integrate computation in users’ daily activities in an unobtrusive way, in order to guarantee an improvement in their experience and quality of life. Furthermore, it is crucial to develop smaller and more intelligent devices to achieve this area’s goals, such as mobility and energy savings. This computing area reinforces the necessity to filter information towards personalization due to its humancentred paradigm. In order to attend to this personalization necessity, it is desired to have a solution that is able to learn the users preferences and needs, resulting in the generation of profiles that represent each style of interaction between a user and an application’s resources(e.g. buttons and menus). Those profiles can be obtained by using machine learning algorithms that use data derived from the user interaction with the application, combined with context data and explicit user preferences. This work proposes an environment with a generic context-aware personalization model and a machine learning module. It is provided the possibility to personalize an application, based on user profiles obtained from data, collected from implicit and explicit user interaction. Using a provided personalization API (Application Programming Interface) and other configuration modules, the environment was tested on LEY (Less energy Empowers You), a persuasive mobile-based serious game to help people understand domestic energy usage.
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SONG, Songbo. "Advanced personalization of IPTV services." Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00814620.

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Internet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users' experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users' devices 'screen types, size, supported resolution, '' and networks available network types to be used by the user, available bandwidth, ..') as well as the context of the service itself (content type and description, available format 'HD/SD', available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users' identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user's environment (devices and networks status), interpreting user's requirements and making the user's interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users' preferences) which in turn highly improves the users' Quality of Experience (QoE) and enriching the offers of IPTV services
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Song, Songbo. "Advanced personalization of IPTV services." Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0001/document.

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Le monde de la TV est en cours de transformation de la télévision analogique à la télévision numérique, qui est capable de diffuser du contenu de haute qualité, offrir aux consommateurs davantage de choix, et rendre l'expérience de visualisation plus interactive. IPTV (Internet Protocol TV) présente une révolution dans la télévision numérique dans lequel les services de télévision numérique sont fournis aux utilisateurs en utilisant le protocole Internet (IP) au dessus d’une connexion haut débit. Les progrès de la technologie IPTV permettra donc un nouveau modèle de fourniture de services. Les fonctions offertes aux utilisateurs leur permettent de plus en plus d’autonomie et de plus en plus de choix. Il en est notamment ainsi de services de type ‘nTS’ (pour ‘network Time Shifting’ en anglais) qui permettent à un utilisateur de visionner un programme de télévision en décalage par rapport à sa programmation de diffusion, ou encore des services de type ‘nPVR’ (pour ‘network Personal Video Recorder’ en anglais) qui permettent d’enregistrer au niveau du réseau un contenu numérique pour un utilisateur. D'autre part, l'architecture IMS proposée dans NGN fournit une architecture commune pour les services IPTV. Malgré les progrès rapides de la technologie de télévision interactive (comprenant notamment les technologies IPTV et NGN), la personnalisation de services IPTV en est encore à ses débuts. De nos jours, la personnalisation des services IPTV se limite principalement à la recommandation de contenus et à la publicité ciblée. Ces services ne sont donc pas complètement centrés sur l’utilisateur, alors que choisir manuellement les canaux de diffusion et les publicités désirées peut représenter une gêne pour l’utilisateur. L’adaptation des contenus numériques en fonction de la capacité des réseaux et des dispositifs utilisés n’est pas encore prise en compte dans les implémentations actuelles. Avec le développement des technologies numériques, les utilisateurs sont amenés à regarder la télévision non seulement sur des postes de télévision, mais également sur des smart phones, des tablettes digitales, ou encore des PCs. En conséquence, personnaliser les contenus IPTV en fonction de l’appareil utilisé pour regarder la télévision, en fonction des capacités du réseau et du contexte de l’utilisateur représente un défi important. Cette thèse présente des solutions visant à améliorer la personnalisation de services IPTV à partir de trois aspects: 1) Nouvelle identification et authentification pour services IPTV. 2) Nouvelle architecture IPTV intégrée et comportant un système de sensibilité au contexte pour le service de personnalisation. 3) Nouveau service de recommandation de contenu en fonction des préférences de l’utilisateur et aussi des informations contextes<br>Internet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users’ experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users’ devices ‘screen types, size, supported resolution, ‘‘ and networks available network types to be used by the user, available bandwidth, ..’) as well as the context of the service itself (content type and description, available format ‘HD/SD’, available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users’ identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user’s environment (devices and networks status), interpreting user’s requirements and making the user’s interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users’ preferences) which in turn highly improves the users’ Quality of Experience (QoE) and enriching the offers of IPTV services
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Rawat, Rakesh. "User behaviour modelling in a multi-dimensional environment for personalization and recommendation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/48135/1/Rakesh_Rawat_Thesis.pdf.

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Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that utilises the outcomes of popular tensor decomposition techniques such as PARAFAC, Tucker and HOSVD to group similar instances. An individual user profile, showing the user's highest interest, is represented by the top dimension values, extracted from the component matrix obtained after tensor decomposition. A group profile, showing similar users and their highest interest, is built by clustering similar users based on tensor decomposed values. A group profile is represented by the top association rules (containing various unique object combinations) that are derived from the searches made by the users of the cluster. An object profile is created to represent similar objects clustered on the basis of their similarity of features. Depending on the category of a user (known, anonymous or frequent visitor to the website), any of the profiles or their combinations is used for making personalized recommendations. A ranking algorithm is also proposed that utilizes the personalized information to order and rank the recommendations. The proposed methodology is evaluated on data collected from a real life car website. Empirical analysis confirms the effectiveness of recommendations made by the proposed approach over other collaborative filtering and content-based recommendation approaches based on two-dimensional data analysis methods.
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Books on the topic "User Personalization"

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Carberry, Sandra, Stephan Weibelzahl, Alessandro Micarelli, and Giovanni Semeraro, eds. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38844-6.

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Houben, Geert-Jan, Gord McCalla, Fabio Pianesi, and Massimo Zancanaro, eds. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02247-0.

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Konstan, Joseph A., Ricardo Conejo, José L. Marzo, and Nuria Oliver, eds. User Modeling, Adaption and Personalization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22362-4.

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Masthoff, Judith, Bamshad Mobasher, Michel C. Desmarais, and Roger Nkambou, eds. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31454-4.

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De Bra, Paul, Alfred Kobsa, and David Chin, eds. User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13470-8.

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Ricci, Francesco, Kalina Bontcheva, Owen Conlan, and Séamus Lawless, eds. User Modeling, Adaptation and Personalization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20267-9.

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Dimitrova, Vania, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, and Geert-Jan Houben, eds. User Modeling, Adaptation, and Personalization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3.

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Heikkinen, Kari. Conceptualization of user-centric personalization management. Lappeenranta University of Technology, 2005.

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Constantinos, Mourlas, and Germanakos Panagiotis, eds. Intelligent user interfaces: Adaptation and personalization systems and technologies. Information Science Reference, 2008.

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Konstan, Joseph A. User Modeling, Adaption and Personalization: 19th International Conference, UMAP 2011, Girona, Spain, July 11-15, 2011. Proceedings. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Book chapters on the topic "User Personalization"

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Manca, Marco, Fabio Paternò, and Carmen Santoro. "Personalization in a Paper Factory." In End-User Development. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79840-6_7.

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Franke, Thomas, and Peter Mertens. "User Modeling and Personalization." In The Customer Centric Enterprise. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55460-5_6.

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Linwood, Jeff, and Dave Minter. "Personalization and User Attributes." In Building Portals with the Java Portlet API. Apress, 2004. http://dx.doi.org/10.1007/978-1-4302-0754-2_11.

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Wasinger, Rainer, Michael Fry, Judy Kay, and Bob Kummerfeld. "User Modelling Ecosystems: A User-Centred Approach." In User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31454-4_31.

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Lehmann, Janette, Mounia Lalmas, Elad Yom-Tov, and Georges Dupret. "Models of User Engagement." In User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31454-4_14.

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Terzi, Maria, Matthew Rowe, Maria-Angela Ferrario, and Jon Whittle. "Text-Based User-kNN: Measuring User Similarity Based on Text Reviews." In User Modeling, Adaptation, and Personalization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3_17.

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Bohnert, Fabian, and Ingrid Zukerman. "A User-and Item-Aware Weighting Scheme for Combining Predictive User Models." In User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13470-8_11.

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Herder, Eelco, Patrick Siehndel, and Ricardo Kawase. "Predicting User Locations and Trajectories." In User Modeling, Adaptation, and Personalization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3_8.

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Plumbaum, Till. "Semantically-Enhanced Ubiquitous User Modeling." In User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13470-8_41.

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Peska, Ladislav. "User Feedback and Preferences Mining." In User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31454-4_41.

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Conference papers on the topic "User Personalization"

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Kostolányová, Kateřina, and Libor Klubal. "Use of user modeling for personalization." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5043719.

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Fallahzadeh, Ramin, and Hassan Ghasemzadeh. "Personalization without user interruption." In ICCPS '17: ACM/IEEE 8th International Conference on Cyber-Physical Systems. ACM, 2017. http://dx.doi.org/10.1145/3055004.3055015.

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Low, Yucheng, Deepak Agarwal, and Alexander J. Smola. "Multiple domain user personalization." In the 17th ACM SIGKDD international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2020408.2020434.

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Wadle, Lisa-Marie, Noemi Martin, and Daniel Ziegler. "Privacy and Personalization." In UMAP '19: 27th Conference on User Modeling, Adaptation and Personalization. ACM, 2019. http://dx.doi.org/10.1145/3314183.3323672.

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Razali, Mohd Norhisham, Tan Soo Fun, Fariza Hanis Abdul Razak, and Rozita Hanapi. "Online information sharing issues in website personalization." In 2010 International Conference on User Science and Engineering (i-USEr 2010). IEEE, 2010. http://dx.doi.org/10.1109/iuser.2010.5716766.

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El-Arini, Khalid, Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, and Blaise Agüera y Arcas. "Transparent user models for personalization." In the 18th ACM SIGKDD international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339639.

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Sowbhagya, M. P., H. K. Yogish, and G. T. Raju. "User Profiling for Web Personalization." In 2022 IEEE International Conference on Data Science and Information System (ICDSIS). IEEE, 2022. http://dx.doi.org/10.1109/icdsis55133.2022.9915969.

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Gervasio, Melinda. "Session details: User modeling and personalization." In IUI '11: 16th International Conference on Intelligent User Interfaces. ACM, 2011. http://dx.doi.org/10.1145/3253021.

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Baeza-Yates, Ricardo. "Personalization, Bias and Privacy." In UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization. ACM, 2020. http://dx.doi.org/10.1145/3386392.3399994.

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Schedl, Markus. "Session details: Personality-Based Personalization." In IUI'17: 22nd International Conference on Intelligent User Interfaces. ACM, 2017. http://dx.doi.org/10.1145/3252645.

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Reports on the topic "User Personalization"

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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it addresses critical ethical considerations, including data privacy, transparency in AI-driven decisions, and reducing algorithmic bias to ensure fair, trustworthy, and responsible marketing practices. Looking ahead, this Article highlights emerging trends like hyper-personalization, ethical AI frameworks, and the integration of AI with technologies like AR/VR and IoT, offering a forward-looking perspective on AI's role in shaping consumer-centric and ethical digital marketing. Keywords: Artificial intelligence, digital marketing, personalized content, ethical advertising, consumer behavior, recommendation engines, predictive analytics, natural language processing, data privacy, transparency, algorithmic bias, hyper-personalization, responsible marketing, AR/VR, IoT, consumer engagement.
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Sarofim, Samer. Developing an Effective Targeted Mobile Application to Enhance Transportation Safety and Use of Active Transportation Modes in Fresno County: The Role of Application Design & Content. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2013.

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This research empirically investigates the need for, and the effective design and content of, a proposed mobile application that is targeted at pedestrians and cyclists in Fresno County. The differential effect of the proposed mobile app name and colors on the target audience opinions was examined. Further, app content and features were evaluated for importance and the likelihood of use. This included design appeal, attractiveness, relevance, ease of navigation, usefulness of functions, personalization and customization, message recipients’ attitudes towards message framing, and intended behaviors related to pedestrian, cyclist, and motorist traffic safety practices. Design mobile application features tested included image aesthetics, coherence and organization, and memorability and distinction. Potential engagement with the mobile app was assessed via measuring the users’ perceived enjoyment while using the app. The behavioral intentions to adopt the app and likelihood to recommend the app were assessed. The willingness to pay for purchasing the app was measured. This research provided evidence that a mobile application designed for pedestrians and cyclists is needed, with high intentions for its adoption. Functions, such as Safety Information, Weather Conditions, Guide to Trails, Events for Walkers and Bikers, and Promotional Offers are deemed important by the target population. This research was conducted in an effort to increase active transportation mode utilization and to enhance the safety of vulnerable road users. The public, city administrators, transportation authorities, and policy makers shall benefit from the results of this study by adapting the design and the features that are proposed in this research and were found appealing and useful for the target vulnerable road user groups. The need of the proposed mobile application and its main functions are established, based on the results of this research, which propagates further steps of implementation by city administrators and transportation authorities.
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Marienko, Maiia V., Yulia H. Nosenko, and Mariya P. Shyshkina. Personalization of learning using adaptive technologies and augmented reality. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4418.

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The research is aimed at developing the recommendations for educators on using adaptive technologies and augmented reality in personalized learning implementation. The latest educational technologies related to learning personalization and the adaptation of its content to the individual needs of students and group work are considered. The current state of research is described, the trends of development are determined. Due to a detailed analysis of scientific works, a retrospective of the development of adaptive and, in particular, cloud-oriented systems is shown. The preconditions of their appearance and development, the main scientific ideas that contributed to this are analyzed. The analysis showed that the scientists point to four possible types of semantic interaction of augmented reality and adaptive technologies. The adaptive cloud-based educational systems design is considered as the promising trend of research. It was determined that adaptability can be manifested in one or a combination of several aspects: content, evaluation and consistency. The cloud technology is taken as a platform for integrating adaptive learning with augmented reality as the effective modern tools to personalize learning. The prospects of the adaptive cloud-based systems design in the context of teachers training are evaluated. The essence and place of assistive technologies in adaptive learning systems design are defined. It is shown that augmented reality can be successfully applied in inclusive education. The ways of combining adaptive systems and augmented reality tools to support the process of teachers training are considered. The recommendations on the use of adaptive cloud-based systems in teacher education are given.
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Spirin, Oleg, and Mariia Shyshkina. Artificial Intelligence in Education and Educational Research: Challenges, Risks, and Prospects for Integration. Institute for Digitalisation of Education of the NAES of Ukraіne, 2025. https://doi.org/10.33407/lib.naes.id/eprint/745119.

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Artificial intelligence (AI) is playing an increasingly important role in education, contributing to the personalization of learning, the automation of administrative processes and educational research. The use of AI allows for increased learning efficiency, improved management of educational resources and improved analytics of educational data. However, the integration of AI into education is accompanied by challenges related, in particular, to technical limitations, pedagogical risks, ethical aspects and security issues. Future research should focus on the integration of AI and augmented and virtual reality technologies, the development of ethical standards and the preparation of teachers and educators to work with new technologies.
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Sierra Noakes, Sierra Noakes, Alison Shell, Alexis M. Murillo, et al. An Ethical and Equitable Vision of AI in Education: Learning Across 28 Exploratory Projects. Digital Promise, 2024. http://dx.doi.org/10.51388/20.500.12265/232.

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This report shares the learnings across 28 exploratory projects from teams across K-12 school districts, nonprofits, and nonprofit and for-profit edtech companies, leveraging AI to support numerous goals across K-12 educational settings. Through this report, we aim to highlight the early successes of AI, surface the key barriers that call for cross-disciplinary and collective problem-solving, and consider the potential for each sector to drive forward an equitable future for AI in education. Preliminary findings from these projects show early evidence of AI’s effectiveness in various tasks, including translation, speech recognition, personalization, organizing and summarizing large qualitative datasets, and streamlining tasks to allow teachers more time with their students. However, these projects also experienced challenges with the current capabilities of AI, often leading to resource- and time-intensive processes, as well as difficulties around adoption and implementation. Additionally, many surfaced concerns around the ethical development and use of AI. Through this work, we have seen exciting ways that cross-sector collaborations are taking shape and gained a large sample of examples that emphasize the need for co-design to build meaningful AI-enabled tools. We call on education leaders, educators, students, product developers, nonprofits, and philanthropic organizations to step back from our day-to-day and imagine a revolutionized education system.
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Osadchyi, Viacheslav V., Hanna B. Varina, Kateryna P. Osadcha, et al. The use of augmented reality technologies in the development of emotional intelligence of future specialists of socionomic professions under the conditions of adaptive learning. CEUR Workshop Proceedings, 2020. http://dx.doi.org/10.31812/123456789/4633.

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In modern conditions, innovative augmented reality technologies are actively developing, which are widespread in many areas of human activity. Introduction of advanced developments in the process of professional training of future specialists of socionomic professions in the conditions of adaptive training, contributes to the implementation of the principles of a personalized approach and increase the overall level of competitiveness. The relevant scientific article is devoted to the theoretical and empirical analysis result of conducting a psychodiagnostic study on an innovative computer complex HC-psychotest. of the features of the implementation of augmented reality technologies in the construct of traditional psychological and pedagogical support aimed at the development of emotional intelligence of the future specialist. The interdisciplinary approach was used while carrying out the research work at the expense of the general fund of the state budget: “Adaptive system for individualization and personalization of professional training of future specialists in the conditions of blended learning”. A comprehensive study of the implementation of traditional psychological-pedagogical and innovative augmented reality technologies was conducted in the framework of scientific cooperation of STEAM-Laboratory, Laboratory of Psychophysiological Research and Laboratory of Psychology of Health in Bogdan Khmelnitsky Melitopol State Pedagogical University. The theoretical analysis considers the structural model of emotional intelligence of the future specialist of socionomic professions, which is represented by two structural components: intrapersonal construct of emotional intelligence and interpersonal construct of emotional intelligence. Each component mediates the inherent emotional intelligence of interpretive, regulatory, adaptive, stress-protective and activating functions. The algorithm of the empirical block of research is presented by two stages: ascertaining and forming research. According to the results of the statement, low indicators were found on most scales, reflecting the general level of emotional intelligence development of future specialists, actualizing the need to find and implement effective measures for the development of emotional intelligence components in modern higher education and taking into account information development and digitalization. As part of the formative stage of the research implementation, a comprehensive program “Development of emotional intelligence of future professionals” was tested, which integrated traditional psychological and pedagogical technologies and innovative augmented reality technologies. This program is designed for 24 hours, 6 thematic classes of 4 hours. According to the results of a comprehensive ascertaining and shaping research, the effectiveness of the influence of augmented reality technologies on the general index of emotional intelligence is proved. The step-by-step model of integration of augmented reality components influencing the ability to analyze, understand and regulate emotional states into a complex program of emotional intelligence development is demonstrated. According to the results of the formative study, there is a dominance of high indicators of the following components: intrapersonal (50%), interpersonal (53.3%). Thus, we can say that intrapersonal and interpersonal emotional intelligence together involve the actualization of various cognitive processes and skills, and are related to each other. Empirical data were obtained as a
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